Staff ML Infrastructure Engineer

General Motors

Staff ML Infrastructure Engineer developing and deploying machine learning solutions. Leading design and implementation of scalable platforms for autonomous vehicle behavior at General Motors.

Posted 6/26/2026full-timeRemote • California • 🇺🇸 United StatesLead💰 $189,300 - $290,700 per yearWebsite

Tech Stack

Tools & technologies

CloudDistributed SystemsDockerKubernetesPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Lead the design, implementation, and deployment of scalable platforms and tools that drive machine learning model training and evaluation workflows across GM.
Own complex technical projects end-to-end, making key architectural decisions and technical trade-offs.
You will be a core contributor to team planning, design reviews, and code quality.
Take a holistic view of projects, considering their impact across multiple teams, and across a longer timeline.
Proactively drive technical prioritization.
Collaborate closely with partner teams to ensure maximum benefit from the systems we build.
Help shape our team through technical interviewing with high, well-calibrated standards, and play an essential role in recruiting.
Mentor and onboard junior engineers and interns, helping them grow their careers.

Requirements

What you’ll need

5+ years of experience building large-scale distributed systems, applications, or advanced ML systems
Proven track record of designing robust frameworks with high-quality, durable APIs
Deep understanding of machine learning algorithms with hands-on application
Expertise in building reliable, high-performance, and cost-efficient systems on modern cloud infrastructure
End-to-end experience across the ML development lifecycle, including MLOps practices
Strong cross functional collaboration skills across teams and organizations
Exceptional coding skills in Python or C++
Strong interest in autonomous driving and its transformative potential
BS, MS, or PhD in Computer Science, Mathematics, or equivalent practical experience.
Nice to have: Experience with distributed training methodologies
Experience scaling ML training across large GPU/CPU clusters or other accelerators
Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow)
Experience with performance profiling and state-of-the-art training optimization techniques, including their impact on model performance
Experience with advanced build systems (e.g., Bazel, Buck, Blaze, CMake)
Proficiency with containerization and orchestration technologies (e.g., Docker, Kubernetes)

Benefits

Comp & perks

medical
dental
vision
Health Savings Account
Flexible Spending Accounts
retirement savings plan
sickness and accident benefits
life insurance
paid vacation & holidays
tuition assistance programs
employee assistance program
GM vehicle discounts and more.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learning algorithmsPythonC++MLOps practicesdistributed systemshigh-quality APIsdeep learning frameworksperformance profilingtraining optimization techniquesdistributed training methodologies

Soft Skills

cross functional collaborationmentoringtechnical interviewingteam planningdesign reviewscode qualityproactive prioritizationholistic project view

Certifications

BS in Computer ScienceMS in Computer SciencePhD in Computer Scienceequivalent practical experience